Posthumanist perspectives on transhumanist marketing: More than human genes, more than market promotion
Bibliographic record
Abstract
Transhumanism advocates enhancement of current human capacities with new technologies, in pursuit of human improvement and perfection, and thereby creates lucrative marketing opportunities. We use the broader concept of posthumanism, which includes this, but also all the other ways in which humans are enhanced by non-humans. However, our study is not about posthumanism, but about how a posthumanist critique can enhance our analyses and diagnoses. We consider not just technology, but also other species such as our microbiome, in an effort to critically examine transhumanist marketing, and develop analytic tools to better understand it. The limitations are highlighted with an extended example of the marketing of health information in response to the Covid-19 pandemic. Transhumanist marketing is distinguished between “ends”, promoting products, and “means”, as ways to facilitate marketing. We offer a typology of motivations for consumption of transhumanist goods and services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.066 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".